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Getting Started

Clipper is a low-latency prediction serving system for machine learning.
Clipper makes it simple to integrate machine learning into user-facing serving systems.

The simplest way to start using Clipper is to use the Clipper Admin Python tool to start a local Clipper cluster using Docker.
Read the container orchestration guide to learn about other ways to run Clipper,
including on Kubernetes.

Install Clipper

Before starting Clipper, you must have a recent version of Docker and Python installed.
We recommend installing Clipper into an Anaconda environment. Clipper currently supports Python 2, 3.5, 3.6.

pip install clipper_admin

Quickstart

First start a Python interpreter session.

# Bare Python interpreter
$ python

# iPython shell
$ conda install ipython
$ ipython

From the Python shell, you can start a new Clipper cluster and deploy a simple Python function as your first model.

from clipper_admin import ClipperConnection, DockerContainerManager

clipper_conn = ClipperConnection(DockerContainerManager())

Start Clipper. Running this command for the first time will
download several Docker containers, so it may take some time.

Query Clipper for predictions

Now that you’ve deployed your first model, you can start requesting predictions with your favorite REST client at the endpoint that Clipper created for your application: http://localhost:1337/hello-world/predict

Clean up

If you closed the Python interpreter session that you used to start Clipper, you will need to start a new Python interpreter session and create another connection to the Clipper cluster. If you still have the interpreter session active from earlier, you can re-use your existing ClipperConnection object.

If you have still have the Python REPL from earlier,
skip directly to clipper_conn.stop_all()